package ds_industry_2025.ds.Formal_volume2.T3

import org.apache.hudi.DataSourceWriteOptions.{PARTITIONPATH_FIELD, PRECOMBINE_FIELD, RECORDKEY_FIELD}
import org.apache.hudi.QuickstartUtils.getQuickstartWriteConfigs
import org.apache.spark.sql.SparkSession
/*
    3、根据dwd_ds_hudi库中的表统计每个省每月下单的数量和下单的总金额，并按照year，month，region_id进行分组,按
    照total_amount降序排序，形成sequence值，将计算结果存入Hudi的dws_ds_hudi数据库province_consumption_day_aggr表中
    （表结构如下），然后使用spark-shell根据订单总数、订单总金额、省份表主键均为降序排序，查询出前5条，在查询时对于订单总金额字段
    将其转为bigint类型（避免用科学计数法展示），将SQL语句复制粘贴至客户端桌面【Release\任务B提交结果.docx】中对应的任务序号下，
    将执行结果截图粘贴至客户端桌面【Release\任务B提交结果.docx】中对应的任务序号下；
 */
object t3 {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder()
      .master("local[*]")
      .appName("t3")
      .config("hive.exec.dynamic.partition.mode","nonstrict")
      .config("spark.serializer","org.apache.spark.serializer.KryoSerializer")
      .config("spark.sql.extensions","org.apache.spark.sql.hudi.HoodieSparkSessionExtension")
      .enableHiveSupport()
      .getOrCreate()

    spark.table("dwd.fact_order_info")
      .where("etl_date=(select max(etl_date) from dwd.fact_order_info)")
      .createOrReplaceTempView("order_info")


    spark.table("dwd.dim_province")
      .where("etl_date=(select max(etl_date) from dwd.dim_province)")
      .createOrReplaceTempView("province")

    spark.table("dwd.dim_region")
      .where("etl_date=(select max(etl_date) from dwd.dim_region)")
      .createOrReplaceTempView("region")


    val result = spark.sql(
      """
        |select
        |uuid() as uuid,
        |*
        |from(
        |select distinct
        |province_id,province_name,
        |region_id,region_name,
        |total_count,total_amount,
        |row_number() over(partition by year,month,region_id,region_name order by total_amount desc) as sequence,
        |year,month
        |from(
        |select distinct
        |o.province_id,
        |p.name as province_name,
        |p.region_id,
        |r.region_name,
        |count(*)
        |over(partition by p.region_id,r.region_name,year(o.create_time),month(o.create_time),o.province_id,p.name) as total_count,
        |sum(o.final_total_amount)
        |over(partition by p.region_id,r.region_name,year(o.create_time),month(o.create_time),o.province_id,p.name) as total_amount,
        |year(o.create_time) as year,
        |month(o.create_time) as month
        |from order_info as o
        |join province as p
        |on p.id=o.province_id
        |join region as r
        |on r.id=p.region_id
        |) as r1
        |) as r2
        |""".stripMargin)


    spark.sql("use dws_ds_hudi")
    spark.sql(
      """
        |create table if not exists province_consumption_day_aggr(
        |uuid String,
        |province_id int,
        |province_name String,
        |region_id int,
        |region_name String,
        |total_amount double,
        |total_count int,
        |sequence int,
        |year int,
        |month int
        |)using hudi
        |tblproperties(
        |type="cow",
        |primaryKey="uuid",
        |preCombineField="total_count",
        |hoodie.datasource.hive_aync.mode="hms"
        |)
        |partitioned by(year,month)
        |""".stripMargin)

    result.write.format("hudi").mode("append")
      .options(getQuickstartWriteConfigs)
      .option(RECORDKEY_FIELD.key(),"uuid")
      .option(PRECOMBINE_FIELD.key(),"total_count")
      .option(PARTITIONPATH_FIELD.key(),"year,month")
      .option("hoodie.table.name","province_consumption_day_aggr")
      .save("hdfs://192.168.40.110:9000/user/hive/warehouse/dws_ds_hudi.db/province_consumption_day_aggr")


    spark.sql("select * from dws_ds_hudi.province_consumption_day_aggr order by total_count desc,total_amount desc,province_id desc limit 5").show





    spark.close()

  }

}
